Because of the complexity of the problem (underlying concepts, expressions in text, etc.), Sentiment Analysis encompasses several separate tasks. These are usually combined to produce some knowledge about the opinions found in text.
The first task is sentiment or opinion detection, which may be viewed as classification of text as objective or subjective. Usually opinion detection is based on the examination of adjectives in sentences. For example, the polarity of the expression “this is a beautiful picture” can be determined easily by looking at the adjective. An early study [HW00] examines the effects of adjectives in sentence subjectivity. More recent studies [BCP+07] have shown that adverbs may be used for similar purpose.
The second task is that of polarity classification. Given an opinionated piece of text, the goal is to classify the opinion as falling under one of two opposing sentiment polarities, or locate its position on the continuum between these two polarities. When viewed as a binary feature, polarity classification is the binary classification task of labeling an opinionated document as expressing either an overall positive or an overall negative opinion.